DSPP Assignment 1_UK
Evelyn
2/9/2020
Loading and Viewing Data
# load packages
library(geosphere)
library(ggplot2)
library(plotly)##
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## layoutlibrary(ggthemes)
library(RColorBrewer)
library(sp)
library(maptools)## Checking rgeos availability: FALSE
## Note: when rgeos is not available, polygon geometry computations in maptools depend on gpclib,
## which has a restricted licence. It is disabled by default;
## to enable gpclib, type gpclibPermit()library(maps)
library(data.table)
library(tidyverse)## -- Attaching packages ---------------------------------- tidyverse 1.3.0 --## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
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## x purrr::map() masks maps::map()
## x purrr::transpose() masks data.table::transpose()library(DT)
library(ggmap)## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.##
## Attaching package: 'ggmap'## The following object is masked from 'package:plotly':
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## windlibrary(mapproj)
library(rgdal)## rgdal: version: 1.4-7, (SVN revision 845)
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## Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
## Path to GDAL shared files: C:/Users/ydeng.AIR/Documents/R/win-library/3.5/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
## Path to PROJ.4 shared files: C:/Users/ydeng.AIR/Documents/R/win-library/3.5/rgdal/proj
## Linking to sp version: 1.3-2library(leaflet)
library(ggrepel)
# set working directory
setwd("C:/Users/ydeng.AIR/Desktop/Evelyn/1. Columbia/04. DSPP/Presentation 1 - Crime")# load data
base::load("police_incidents_df.rdata")
#base::load("uk_aggregated.rdata")
#base::load("uk_aggregated_anti_social.rdata")
# view data
head(police_incidents,10)#head(uk_aggregated,1000)
#head(uk_aggregated_anti_social, 1000) Analysis of Crime by Crime Type
Data Cleaning and Restructuring
# choose only necessary variables
crime <- police_incidents[c(2,4,5,6,10)]
crimetable(crime$Crime.type)##
## Anti-social behaviour Bicycle theft
## 5507770 272692
## Burglary Criminal damage and arson
## 1254892 1702860
## Drugs Other crime
## 430853 220388
## Other theft Possession of weapons
## 1531002 91439
## Public order Robbery
## 755325 174401
## Shoplifting Theft from the person
## 1077225 257796
## Vehicle crime Violence and sexual offences
## 1193693 3615919# create dataset with aggregated crime by month by type
crime_freq_by_month <- crime %>%
group_by(Month, Crime.type) %>%
summarise(frequency = n())
crime_freq_by_monthVisualize Data: Crime by Month and Type
graph <- ggplot(crime_freq_by_month, aes(x = Month, y = frequency)) +
geom_line(aes(group=Crime.type, color = Crime.type)) +
#geom_point(size = 2, shape = 21, fill = "lightblue") +
theme(legend.position = "top") +
labs(x="Month", y="Number of Crimes") +
ggtitle("Monthly Crime Frequency in the UK") +
theme_hc() + theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplotly(graph, tooltip = c("x", "y", "group"), dynamicTicks=TRUE)Insights
ASB is the most frequent crime, followed by violent and sexual offenses
seasonal crime peaks: - anti-social behavior(ASB) summer - violent and sexual offences summer - other theft summer - bicycle theft summer - public order summer - burglary fall to early winter - theft from the person early winter (December)
the seasonality of crime is most notable for: - ASB (approx 45% more crime during peak season) - bike theft (approx 100% more crime during peak season)
Note: Increase in crime is not a reflection of increase in population, which was only about 2% from 2014 to 2017 (office for national statistics: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/articles/overviewoftheukpopulation/august2019)
Data Table of Change in Crime by Crime Type
# TODO: reshape data so that columns are "Crime Type" "Change Y1" "Change Y2" "Change Y3", "Change Overall". create a data table or line graph showing crimes that have an overall % change of more than 10%, include "all"
# export data to excel, where % change per crime type is calculated
# for year-to-year and over the three years and new dataset is read in
write_csv(crime_freq_by_month, "crime_freq_by_month.csv")
crime_change_by_type <- read_csv("crime_change_by_type.csv")## Parsed with column specification:
## cols(
## `Time Period` = col_character(),
## `# of Incidents: Beginning of Time Period` = col_double(),
## `# of Incidents: End of Time Period` = col_double(),
## `Crime Type` = col_character(),
## `Change in # of Incidents` = col_double(),
## `% Change` = col_double(),
## `Absolute % Change` = col_double()
## )# reduce dataset down to relevant variables and only 8 types with largest absolute change in crime
crime_change_by_type <- crime_change_by_type %>%
.[,c(1,4,6)]
top_change_types <- crime_change_by_type$`Crime Type`[1:8]
top_change_tbl <- crime_change_by_type[crime_change_by_type$`Crime Type` %in% top_change_types, ]
# make "Crime Type" an ordered factor variable for graphing
top_change_tbl$`Crime Type` <- factor(top_change_tbl$`Crime Type`, levels= top_change_tbl$`Crime Type`[1:8])
top_change_tbllibrary(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
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## col_factorp <- ggplot(top_change_tbl, aes(x=fct_rev(`Crime Type`), y=`% Change`, fill=`Time Period`)) +
geom_bar(position="dodge", stat="identity") + coord_flip() +
scale_fill_manual(values=c( "#c5eb7f","#9ACA3C", "#71a803", "#003F5D")) +
theme_tufte() + guides(fill = guide_legend(reverse=TRUE)) +
scale_y_continuous(limits = c(-0.25, 1.2), labels = percent,
breaks = c(-0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2)) +
labs(fill = NULL, x = NULL, y = "Percent Increase", title = "Top Changes in Crime (2014-2017)") +
theme(axis.ticks = element_blank(), panel.grid.major.x = element_line(color = "light grey"),
legend.position = "bottom", legend.text=element_text(size=12), axis.text=element_text(size=12))
p
Analysis of Crime by Location
# TODO:
# similar analysis of data as for crime by type above# TODO:
# chose a subset of data
# map data
# - heat map of all data (one or across time? show seasonality for certain crime types?)
# - map with pins of subsetted datalibrary(rgdal)
ukpp_original <- readOGR(getwd(),"ukpp") ## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\ydeng.AIR\Desktop\Evelyn\1. Columbia\04. DSPP\Presentation 1 - Crime", layer: "ukpp"
## with 43 features
## It has 9 fields
## Integer64 fields read as strings: objectid bng_e bng_nukpp_transformed <- spTransform(ukpp_original, CRS("+proj=longlat +datum=WGS84"))
ukpp <- fortify(ukpp_transformed)## Regions defined for each Polygonsunique(ukpp$id)## [1] "0" "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14"
## [16] "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29"
## [31] "30" "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42"# find centers of counties
county_labels <- ukpp %>%
group_by(id) %>%
summarise(lat_center = mean(lat), long_center=mean(long))
county_labelscounty_labels <- county_labels %>%
mutate(county = case_when(
id==0~'Surrey ',
id==1~'Cumbria ', id==2~'Lancashire ',
id==3~'Merseyside ', id==4~'Greater Manchester ',
id==5~'Cheshire ',
id==6~'Northumbria ', id==7~'Durham ',
id==8~'North Yorkshire ', id==9~'West Yorkshire ',
id==10~'South Yorkshire ', id==11~'Humberside ',
id==12~'Cleveland ', id==13~'West Midlands ',
id==14~'Staffordshire ', id==15~'West Mercia ',
id==16~'Warwickshire ', id==17~'Derbyshire ',
id==18~'Nottinghamshire ', id==19~'Lincolnshire ',
id==20~'Leicestershire ', id==21~'Northamptonshire ',
id==22~'Cambridgeshire ', id==23~'Norfolk ',
id==24~'Suffolk ', id==25~'Bedfordshire ',
id==26~'Hertfordshire ', id==27~'Essex ',
id==28~'Thames Valley ', id==29~'Hampshire ',
id==30~'Metropolitan ', id==31~'Kent ',
id==32~'Sussex ', id==33~'City of London ',
id==34~'Devon & Cornwall ', id==35 ~'Avon and Somerset ',
id==36~'Gloucestershire ', id==37~'Wiltshire ',
id==38~'North Wales ', id==39~'North Wales ',
id==40~'Gwent ', id==41~'South Wales ',
id==42~'Dyfed-Powys '))
county_labels$id <- NULL
county_labelsukpp$id <-as.numeric(ukpp$id)Recode id in the nypp dataset to reflect precinct numbers.
# shape file does not include two police forces listed in data: "British Transport Police" and "Police Service of Nothern Ireland"
# replace ids with police force names
ukpp2 <- ukpp %>%
mutate(county = case_when(
id==0~'Surrey Police',
id==1~'Cumbria Constabulary', id==2~'Lancashire Constabulary',
id==3~'Merseyside Police', id==4~'Greater Manchester Police',
id==5~'Cheshire Constabulary',
id==6~'Northumbria Police', id==7~'Durham Constabulary',
id==8~'North Yorkshire Police', id==9~'West Yorkshire Police',
id==10~'South Yorkshire Police', id==11~'Humberside Police',
id==12~'Cleveland Police', id==13~'West Midlands Police',
id==14~'Staffordshire Police', id==15~'West Mercia Police',
id==16~'Warwickshire Police', id==17~'Derbyshire Constabulary',
id==18~'Nottinghamshire Police', id==19~'Lincolnshire Police',
id==20~'Leicestershire Police', id==21~'Northamptonshire Police',
id==22~'Cambridgeshire Constabulary', id==23~'Norfolk Constabulary',
id==24~'Suffolk Constabulary', id==25~'Bedfordshire Police',
id==26~'Hertfordshire Constabulary', id==27~'Essex Police',
id==28~'Thames Valley Police', id==29~'Hampshire Constabulary',
id==30~'Metropolitan Police', id==31~'Kent Police',
id==32~'Sussex Police', id==33~'City of London Police',
id==34~'Devon & Cornwall Police', id==35 ~'Avon and Somerset Constabulary',
id==36~'Gloucestershire Constabulary', id==37~'Wiltshire Police',
id==38~'North Wales Police', id==39~'North Wales Police',
id==40~'Gwent Police', id==41~'South Wales Police',
id==42~'Dyfed-Powys Police'))
ukpp2$id <- NULL
ukpp2# create dataset with aggregated crime by month by type by region
`%notin%` <- Negate(`%in%`)
crime$Falls.within <- as.character(crime$Falls.within)
crime[!is.na(crime$Falls.within) & crime$Falls.within=="Metropolitan Police Service", 2] <- "Metropolitan Police"
crime_by_region <- crime %>%
filter(Falls.within %notin% c("Police Service of Nothern Ireland")) %>% #not in shape file
group_by(Month, Crime.type, Falls.within) %>%
summarise(frequency = n()) %>%
spread(Crime.type, frequency)
crime_by_region$all = rowSums(subset(crime_by_region, select=`Anti-social behaviour`:`Violence and sexual offences`))
crime_by_region <- crime_by_region %>% filter(Falls.within!="City of London Police")Seasonal Analysis of ASB
# read in population data
pop <- read_csv("Population by year.csv")## Parsed with column specification:
## cols(
## county = col_character(),
## pop2014 = col_double(),
## pop2015 = col_character(),
## pop2016 = col_double()
## )ukpp_year14 <- left_join(pop, ukpp2, by = "county")[,c(1,2,5:10)]
ukpp_year14ukpp_year15 <- left_join(pop, ukpp2, by = "county")[,c(1,3,5:10)]
ukpp_year15$pop2015 <- as.numeric(ukpp_year15$pop2015)## Warning: NAs introduced by coercionukpp_year16 <- left_join(pop, ukpp2, by = "county")[,c(1,4,5:10)]
ukpp_year16#ASB 2015-07
crime_by_region_201501 <- crime_by_region %>%
filter(Month=="2015-01")
ukpp_crime_201501 <- left_join(crime_by_region_201501, ukpp_year15, by = c("Falls.within" = "county"))
ukpp_crime_201501 <- transform(ukpp_crime_201501, ABS_Crime_Rate = `Anti-social behaviour`*1000 / pop2015)
ukpp_crime_201501 <- ukpp_crime_201501[,c(1,2,3,18:25)]
ukpp_crime_201501#ASB 2015-07
crime_by_region_201507 <- crime_by_region %>%
filter(Month=="2015-07")
ukpp_crime_201507 <- left_join(crime_by_region_201507, ukpp_year15, by = c("Falls.within" = "county"))
ukpp_crime_201507 <- transform(ukpp_crime_201507, ABS_Crime_Rate = `Anti-social behaviour`*1000 / pop2015)
ukpp_crime_201507 <- ukpp_crime_201507[,c(1,2,3,18:25)]
ukpp_crime_201507#ASB 2016-01
crime_by_region_201601 <- crime_by_region %>%
filter(Month=="2016-01")
ukpp_crime_201601 <- left_join(crime_by_region_201601, ukpp_year16, by = c("Falls.within" = "county"))
ukpp_crime_201601 <- transform(ukpp_crime_201601, ABS_Crime_Rate = `Anti-social behaviour`*1000 / pop2016)
ukpp_crime_201601 <- ukpp_crime_201601[,c(1,2,3,18:25)]
ukpp_crime_201601#ASB 2016-07
crime_by_region_201607 <- crime_by_region %>%
filter(Month=="2016-07")
ukpp_crime_201607 <- left_join(crime_by_region_201607, ukpp_year16, by = c("Falls.within" = "county"))
ukpp_crime_201607 <- transform(ukpp_crime_201607, ABS_Crime_Rate = `Anti-social behaviour`*1000 / pop2016)
ukpp_crime_201607 <- ukpp_crime_201607[,c(1,2,3,18:25)]
ukpp_crime_201607Plot ASB in UK for Winters and Summers 2015-2016 (not including London)
#winter 2015
ggplot() +
geom_polygon(data = ukpp_crime_201501, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201501$ABS_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", breaks=c(0, 2,4,6,8,10), limits = c(0, 10),
low = "#008cff", high = "#ff1414") +theme_bw() 
#summer2015
ggplot() +
geom_polygon(data = ukpp_crime_201507, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201507$ABS_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", breaks=c(0, 2,4,6,8,10), limits = c(0, 10),
low = "#008cff", high = "#ff1414") +theme_bw() 
#winter 2016
ggplot() +
geom_polygon(data = ukpp_crime_201601, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201601$ABS_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", breaks=c(0, 2,4,6,8,10), limits = c(0, 10),
low = "#008cff", high = "#ff1414") +theme_bw() 
#summer 2016
ggplot() +
geom_polygon(data = ukpp_crime_201607, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201607$ABS_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", breaks=c(0, 2,4,6,8,10), limits = c(0, 10),
low = "#008cff", high = "#ff1414") +theme_bw() 
Seasonal Analysis of Bike Theft
#Bicycle theft 2015-07
crime_by_region_201501 <- crime_by_region %>%
filter(Month=="2015-01")
ukpp_crime_201501 <- left_join(crime_by_region_201501, ukpp_year15, by = c("Falls.within" = "county"))
ukpp_crime_201501 <- transform(ukpp_crime_201501, BT_Crime_Rate = `Bicycle theft`*1000 / pop2015)
ukpp_crime_201501 <- ukpp_crime_201501[,c(1,2,4,18:25)]
ukpp_crime_201501#Bicycle theft 2015-07
crime_by_region_201507 <- crime_by_region %>%
filter(Month=="2015-07")
ukpp_crime_201507 <- left_join(crime_by_region_201507, ukpp_year15, by = c("Falls.within" = "county"))
ukpp_crime_201507 <- transform(ukpp_crime_201507, BT_Crime_Rate = `Bicycle theft`*1000 / pop2015)
ukpp_crime_201507 <- ukpp_crime_201507[,c(1,2,4,18:25)]
ukpp_crime_201507#Bicycle theft 2016-01
crime_by_region_201601 <- crime_by_region %>%
filter(Month=="2016-01")
ukpp_crime_201601 <- left_join(crime_by_region_201601, ukpp_year16, by = c("Falls.within" = "county"))
ukpp_crime_201601 <- transform(ukpp_crime_201601, BT_Crime_Rate = `Bicycle theft`*1000 / pop2016)
ukpp_crime_201601 <- ukpp_crime_201601[,c(1,2,4,18:25)]
ukpp_crime_201601#Bicycle theft 2016-07
crime_by_region_201607 <- crime_by_region %>%
filter(Month=="2016-07")
ukpp_crime_201607 <- left_join(crime_by_region_201607, ukpp_year16, by = c("Falls.within" = "county"))
ukpp_crime_201607 <- transform(ukpp_crime_201607, BT_Crime_Rate = `Bicycle theft`*1000 / pop2016)
ukpp_crime_201607 <- ukpp_crime_201607[,c(1,2,4,18:25)]
ukpp_crime_201607Plot Bike theft in UK for Winters and Summers 2015-2016 (not including London)
#winter 2015
ggplot() +
geom_polygon(data = ukpp_crime_201501, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201501$BT_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", limits = c(0, 0.43),
low = "#008cff", high = "#ff1414") +theme_bw() 
#summer2015
ggplot() +
geom_polygon(data = ukpp_crime_201507, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201507$BT_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", limits = c(0, 0.43),
low = "#008cff", high = "#ff1414") +theme_bw() 
#winter 2016
ggplot() +
geom_polygon(data = ukpp_crime_201601, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201601$BT_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", limits = c(0, 0.43),
low = "#008cff", high = "#ff1414") +theme_bw() 
#summer 2016
ggplot() +
geom_polygon(data = ukpp_crime_201607, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201607$BT_Crime_Rate)) +
scale_fill_gradient(name="Crimes per Thousand", limits = c(0, 0.43),
low = "#008cff", high = "#ff1414") +theme_bw() 
Analysis of Top Changes in Crime
#no major changes in population in any PFA (most change was 3% from 2014 to 2016), so I'll just use the raw counts.
#Public order 2014-10
crime_by_region_201410 <- crime_by_region %>%
filter(Month=="2014-10")
ukpp_crime_201410 <- left_join(crime_by_region_201410, ukpp2, by = c("Falls.within" = "county"))
ukpp_crime_201410 <- ukpp_crime_201410[,c(1,2,11,18:23)]
ukpp_crime_201410#Public order 2017-09
crime_by_region_201709 <- crime_by_region %>%
filter(Month=="2017-09")
ukpp_crime_201709 <- left_join(crime_by_region_201709, ukpp2, by = c("Falls.within" = "county"))
ukpp_crime_201709 <- ukpp_crime_201709[,c(1,2,11,18:23)]
ukpp_crime_201709#Posession of weapons 2014-10
crime_by_region_201410 <- crime_by_region %>%
filter(Month=="2014-10")
ukpp_weapons_201410 <- left_join(crime_by_region_201410, ukpp2, by = c("Falls.within" = "county"))
ukpp_weapons_201410 <- ukpp_weapons_201410[,c(1,2,10,18:23)]
ukpp_weapons_201410#Posession of weapons 2017-09
crime_by_region_201709 <- crime_by_region %>%
filter(Month=="2017-09")
ukpp_weapons_201709 <- left_join(crime_by_region_201709, ukpp2, by = c("Falls.within" = "county"))
ukpp_weapons_201709 <- ukpp_weapons_201709[,c(1,2,10,18:23)]
ukpp_weapons_201709Plot public order in the UK for October 2014 and September 2017 (not including London)
# 2014-10
ggplot() +
geom_polygon(data = ukpp_crime_201410, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201410$`Public order`)) +
scale_fill_gradient(name="Number of Crimes", limits = c(0, 4500),
low = "#ffffff", high = "firebrick") +theme_bw() 
# 2017-09
ggplot() +
geom_polygon(data = ukpp_crime_201709, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201709$`Public order`)) +
scale_fill_gradient(name="Number of Crimes", limits = c(0, 4500),
low = "#ffffff", high = "firebrick") +theme_bw() 
Plot public order in the UK for October 2014 and September 2017 (not including London)
# 2014-10
ggplot() +
geom_polygon(data = ukpp_weapons_201410, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_weapons_201410$`Possession of weapons`)) +
scale_fill_gradient(name="Number of Crimes", limits = c(0, 600),
low = "#ffffff", high = "firebrick") +theme_bw() 
# 2017-09
ggplot() +
geom_polygon(data = ukpp_weapons_201709, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_weapons_201709$`Possession of weapons`)) +
scale_fill_gradient(name="Number of Crimes", limits = c(0, 600),
low = "#ffffff", high = "firebrick") +theme_bw() 
Westminster Attacks Analysis
#2016-02
crime_by_region_201602 <- crime_by_region %>%
filter(Month=="2016-02")
crime_by_region_201602ukpp_crime_201602 <- left_join(crime_by_region_201602, ukpp2, by = c("Falls.within" = "county"))
#2016-03
crime_by_region_201603 <- crime_by_region %>%
filter(Month=="2016-03")
crime_by_region_201603ukpp_crime_201603 <- left_join(crime_by_region_201603, ukpp2, by = c("Falls.within" = "county"))
#==================================== 2017 Feb-March =======================================
#2017-02
crime_by_region_201702 <- crime_by_region %>%
filter(Month=="2017-02")
crime_by_region_201702ukpp_crime_201702 <- left_join(crime_by_region_201702, ukpp2, by = c("Falls.within" = "county"))
#2017-03
crime_by_region_201703 <- crime_by_region %>%
filter(Month=="2017-03")
crime_by_region_201703ukpp_crime_201703 <- left_join(crime_by_region_201703, ukpp2, by = c("Falls.within" = "county"))Get the raster map background for UK
UK_map <- get_map(c(left = -6.5, bottom = 49.5, right = 3, top = 56),
source = "stamen", maptype="toner-lite")## Source : http://tile.stamen.com/terrain/7/61/39.png## Source : http://tile.stamen.com/terrain/7/62/39.png## Source : http://tile.stamen.com/terrain/7/63/39.png## Source : http://tile.stamen.com/terrain/7/64/39.png## Source : http://tile.stamen.com/terrain/7/65/39.png## Source : http://tile.stamen.com/terrain/7/61/40.png## Source : http://tile.stamen.com/terrain/7/62/40.png## Source : http://tile.stamen.com/terrain/7/63/40.png## Source : http://tile.stamen.com/terrain/7/64/40.png## Source : http://tile.stamen.com/terrain/7/65/40.png## Source : http://tile.stamen.com/terrain/7/61/41.png## Source : http://tile.stamen.com/terrain/7/62/41.png## Source : http://tile.stamen.com/terrain/7/63/41.png## Source : http://tile.stamen.com/terrain/7/64/41.png## Source : http://tile.stamen.com/terrain/7/65/41.png## Source : http://tile.stamen.com/terrain/7/61/42.png## Source : http://tile.stamen.com/terrain/7/62/42.png## Source : http://tile.stamen.com/terrain/7/63/42.png## Source : http://tile.stamen.com/terrain/7/64/42.png## Source : http://tile.stamen.com/terrain/7/65/42.png## Source : http://tile.stamen.com/terrain/7/61/43.png## Source : http://tile.stamen.com/terrain/7/62/43.png## Source : http://tile.stamen.com/terrain/7/63/43.png## Source : http://tile.stamen.com/terrain/7/64/43.png## Source : http://tile.stamen.com/terrain/7/65/43.pngbackground_UK <- ggmap(UK_map)
background_UK
Plot ASB in UK 2016-02
library(ggrepel)
background_UK +
geom_polygon(data = ukpp_crime_201602, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201602$`Anti-social behaviour`)) +
scale_fill_gradient(name="Number of Crimes",
low = "#fff7f7", high = "firebrick") +
geom_text_repel(data = county_labels, inherit.aes = FALSE, size = 3,
aes(label=county, x = long_center, y = lat_center))## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf
Plot ASB in UK 2016-03
library(ggrepel)
background_UK +
geom_polygon(data = ukpp_crime_201603, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201603$`Anti-social behaviour`)) +
scale_fill_gradient(name="Number of Crimes",
low = "#fff7f7", high = "firebrick") +
geom_text_repel(data = county_labels, inherit.aes = FALSE, size = 3,
aes(label=county, x = long_center, y = lat_center))## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf
Plot ASB in UK 2017-02
library(ggrepel)
background_UK +
geom_polygon(data = ukpp_crime_201702, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201702$`Anti-social behaviour`)) +
scale_fill_gradient(name="Number of Crimes",
low = "#fff7f7", high = "firebrick") +
geom_text_repel(data = county_labels, inherit.aes = FALSE, size = 3,
aes(label=county, x = long_center, y = lat_center))## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf
Plot ASB in UK 2017-03
library(ggrepel)
background_UK +
geom_polygon(data = ukpp_crime_201703, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_201703$`Anti-social behaviour`)) +
scale_fill_gradient(name="Number of Crimes",
low = "#fff7f7", high = "firebrick") +
geom_text_repel(data = county_labels, inherit.aes = FALSE, size = 3,
aes(label=county, x = long_center, y = lat_center))## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf
Crime Rate by Police Force Area
crime_rate <- read_csv("Crime rate by region.csv")## Parsed with column specification:
## cols(
## County = col_character(),
## CrimeRate = col_double()
## )ukpp_crime_rate <- left_join(ukpp2, crime_rate, by = c("county" = "County"))
ukpp_crime_ratelondon_crime <- ukpp_crime_rate[ukpp_crime_rate$county == "City of London Police",]
london_crimePlot Crime Rate in UK 2016
library(ggrepel)
ggplot() +
geom_polygon(data = ukpp_crime_rate, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =ukpp_crime_rate$CrimeRate)) +
scale_fill_gradient(name="Crime Rate per Thousand",
low = "#fff7f7", high = "firebrick") +
geom_text_repel(data = county_labels, inherit.aes = FALSE, size = 5,
aes(label=county, x = long_center, y = lat_center)) + theme_bw()
no_london_crime <- ukpp_crime_rate[ukpp_crime_rate$county != "City of London Police",]
ggplot() +
geom_polygon(data = no_london_crime, color = "dark grey", size = 0.4,
aes(x = long, y = lat, group=group, fill =no_london_crime$CrimeRate)) +
scale_fill_gradient(name="Crime Rate per Thousand",
low = "#fff7f7", high = "firebrick") +
geom_text_repel(data = county_labels, inherit.aes = FALSE, size = 5,
aes(label=county, x = long_center, y = lat_center)) +theme_bw()
Interactive Maps of London Crimes
# filter all London crimes by month and get rid of "other crime" category
London_201709_df <- police_incidents %>%
filter(Month=="2017-09", Falls.within=="City of London Police", Crime.type != "Other crime") %>%
.[, c(2,4,5,6,10)] %>%
mutate(agg.crime.type = case_when(
Crime.type %in% c("Bicycle theft", "Other theft", "Shoplifting", "Theft from the person") ~'Theft',
Crime.type=="Burglary"~'Burglary',
Crime.type=="Criminal damage and arson"~'Criminal damage and arson',
Crime.type=="Vehicle crime"~'Vehicle crime',
Crime.type=="Violence and sexual offences"~'Violence and sexual offences',
Crime.type %in% c("Drugs", "Possession of weapons") ~'Drugs or weapon possession',
Crime.type=="Public order"~'Public order',
Crime.type=="Robbery"~'Robbery')) %>%
arrange(Longitude)
London_201709_df$Crime.type <- as.character(London_201709_df$Crime.type)
London_201709_dfLondon 2017-09 Interactive Map
# make color palette
library(RColorBrewer)
palette = colorFactor("Accent", domain = London_201709_df$agg.crime.type) # Grab a palette
color_crime_type = palette(London_201709_df$agg.crime.type)
# make up popup content
content <- paste("Type of Crime: ", London_201709_df$agg.crime.type, "<br/>",
"Month of Incident: ", London_201709_df$Month)
# create map for London crimes in 2017-09
London_201709_map <- leaflet(London_201709_df) %>%
addProviderTiles("Stamen.TonerLite") %>%
addCircles(col= rev(color_crime_type), opacity=0.3,
popup = content,
highlightOptions = highlightOptions(
color='#0061ff', weight = 5,
bringToFront = TRUE, sendToBack = TRUE)) %>%
setView(lng = -0.095, lat = 51.515, zoom = 15)## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively## Warning in validateCoords(lng, lat, funcName): Data contains 47 rows with either
## missing or invalid lat/lon values and will be ignoredLondon_201709_map # filter to London thefts in all of 2016
London_theft_2016_df <- police_incidents %>%
filter(Falls.within=="City of London Police",
Crime.type %in% c("Public order"),
Month %in% c("2016-01", "2016-02", "2016-03", "2016-04", "2016-05", "2016-06",
"2016-07", "2016-08", "2016-09", "2016-10", "2016-11", "2016-12")
) %>%
.[, c(2,4,5,6,10)] %>%
arrange(Crime.type)
London_theft_2016_df$Crime.type <- as.character(London_theft_2016_df$Crime.type)
London_theft_2016_dfLondon 2016 Theft Interactive Map
# make color palette
library(RColorBrewer)
palette = colorFactor("Set1", domain = London_theft_2016_df$Crime.type) # Grab a palette
color_crime_type = palette(London_theft_2016_df$Crime.type)
# make up popup content
content <- paste("Type of Crime: ", London_theft_2016_df$Crime.type, "<br/>",
"Month of Incident: ", London_theft_2016_df$Month)
# create map for London thefts in 2016
London_theft_2016_map <- leaflet(London_theft_2016_df) %>%
addProviderTiles("Stamen.TonerLite") %>%
addCircles(col= color_crime_type, opacity=0.1,
popup = content,
highlightOptions = highlightOptions(
color='#0061ff', weight = 5,
bringToFront = TRUE, sendToBack = TRUE)) %>%
setView(lng = -0.095, lat = 51.515, zoom = 15)## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively## Warning in validateCoords(lng, lat, funcName): Data contains 22 rows with either
## missing or invalid lat/lon values and will be ignoredLondon_theft_2016_map # filter to London crimes before and after Westminster attacks
Westminster_feb_df <- police_incidents %>%
filter(Falls.within=="Metropolitan Police Service",
Crime.type %in% c("Anti-social behaviour"),
Month == "2017-02"
) %>%
.[, c(2,4,5,6,10)] %>%
arrange(Crime.type)
Westminster_feb_df$Crime.type <- as.character(Westminster_feb_df$Crime.type)
Westminster_feb_dfWestminster Interactive Map
# make up popup content
content <- paste("Type of Crime: ", Westminster_feb_df$Crime.type, "<br/>",
"Month of Incident: ", Westminster_feb_df$Month)
# create map for London thefts in 2016
Westminster_feb_map <- leaflet(Westminster_feb_df) %>%
addProviderTiles("Stamen.TonerLite") %>%
addCircles(col= "red", opacity=0.2,
popup = content,
highlightOptions = highlightOptions(
color='#0061ff', weight = 5,
bringToFront = TRUE, sendToBack = TRUE)) %>%
setView(lng = -0.129, lat = 51.4994827, zoom = 15)## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively## Warning in validateCoords(lng, lat, funcName): Data contains 5 rows with either
## missing or invalid lat/lon values and will be ignoredWestminster_feb_map # filter to London crimes before and after Westminster attacks
Westminster_mar_df <- police_incidents %>%
filter(Falls.within=="Metropolitan Police Service",
Crime.type %in% c("Anti-social behaviour"),
Month == "2017-03"
) %>%
.[, c(2,4,5,6,10)] %>%
arrange(Crime.type)
Westminster_mar_df$Crime.type <- as.character(Westminster_mar_df$Crime.type)
Westminster_mar_dfWestminster Interactive Map - March
# make up popup content
content <- paste("Type of Crime: ", Westminster_mar_df$Crime.type, "<br/>",
"Month of Incident: ", Westminster_mar_df$Month)
# create map for London thefts in 2016
Westminster_mar_map <- leaflet(Westminster_mar_df) %>%
addProviderTiles("Stamen.TonerLite") %>%
addCircles(col= "red", opacity=0.2,
popup = content,
highlightOptions = highlightOptions(
color='#0061ff', weight = 5,
bringToFront = TRUE, sendToBack = TRUE)) %>%
setView(lng = -0.129, lat = 51.4994827, zoom = 15)## Assuming "Longitude" and "Latitude" are longitude and latitude, respectivelyWestminster_mar_map # filter to London crimes before and after Westminster attacks
Westminster_apr_df <- police_incidents %>%
filter(Falls.within=="Metropolitan Police Service",
Crime.type %in% c("Anti-social behaviour"),
Month == "2017-04"
) %>%
.[, c(2,4,5,6,10)] %>%
arrange(Crime.type)
Westminster_apr_df$Crime.type <- as.character(Westminster_apr_df$Crime.type)
Westminster_apr_dfWestminster Interactive Map - April
# make up popup content
content <- paste("Type of Crime: ", Westminster_apr_df$Crime.type, "<br/>",
"Month of Incident: ", Westminster_apr_df$Month)
# create map for London thefts in 2016
Westminster_apr_map <- leaflet(Westminster_apr_df) %>%
addProviderTiles("Stamen.TonerLite") %>%
addCircles(col= "red", opacity=0.2,
popup = content,
highlightOptions = highlightOptions(
color='#0061ff', weight = 5,
bringToFront = TRUE, sendToBack = TRUE)) %>%
setView(lng = -0.129, lat = 51.4994827, zoom = 15)## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively## Warning in validateCoords(lng, lat, funcName): Data contains 5 rows with either
## missing or invalid lat/lon values and will be ignoredWestminster_apr_map